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Convolutional Neural Networks (CNNs) Tutorial with Python
An in-depth tutorial on convolutional neural networks (CNNs) with Python
Last updated, January 8, 2021
Author(s): Saniya Parveez, Roberto Iriondo
This tutorial’s code is available on Github and its full implementation as well on Google Colab.
Table of Contents
- Introduction
- Network Architecture
- Convolution
- Convolutional Layers
- Pooling Layers/Subsampling layers
- Stride
- Fully Connected Layer
- Non-Linear Layers
- Python Implementation of Convolutional Neural Networks (CNNs)
- Hyperparameters for CNNs
- Regularization Methods in CNNs
- Conclusion
- Resources
- References
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Introduction
Yann LeCun and Yoshua Bengio introduced convolutional neural networks in 1995 [1], also known as convolutional networks or CNNs. A CNN is a particular kind of multi-layer neural network [2] to process data with an apparent, grid-like topology. The base of its network bases on a mathematical operation called convolution. Fundamentally, machine learning algorithms use matrix multiplication, but in contrast, CNNs use convolutions in place of matrix multiplications at least in one layer — a convolution is a specialized kind of linear operation.
Convolutional neural networks (CNNs) are undoubtedly the most popular deep learning architecture. Their applications are everywhere, including image…
